RuntimeError: Trying to create tensor with negative dimension -25: [-25]

训练过程报错

为了测试，我收集了一份简单的日志，并按照要求把文件转换成recbole指定的格式，但是运行的时候报错:RuntimeError: Trying to create tensor with negative dimension -25: [-25]
另外时间戳的单位是（s or ms？）

以下是我的代码

test.yaml

USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
RATING_FIELD: rating
TIME_FIELD: timestamp
load_col:
    inter: [rating, user_id, item_id, timestamp]

min_user_inter_num: 10
min_item_inter_num: 10
lowest_val:
    rating: 2
eval_setting: RO_RS,full
split_ratio: [0.8,0.1,0.1]

run.py

from recbole.config import Config
from recbole.data import create_dataset,data_preparation
from recbole.model.general_recommender import BPR
from recbole.trainer import Trainer
from logging import  getLogger
from recbole.utils import init_seed,init_logger

if __name__ == '__main__':
    # 初始化配置
    config = Config(model='BPR',dataset='test')
    config['data_path'] = './test_data/'
    config['dataset'] = 'test'
    # 初始随机种子，确保实验的可重复性
    init_seed(config['seed'],config['reproducibility'])
    ## 初始化日志
    init_logger(config)
    logger = getLogger()
    ## 将配置信息写入日志
    logger.info(config)
    ## 数据集创建和筛选
    dataset = create_dataset(config)
    logger.info(dataset)
    ## 数据集划分
    train_data,valid_data,test_data = data_preparation(config,dataset)

    ## 模型载入和初始化
    model = BPR(config,train_data).to(config['device'])
    logger.info(model)
    ## 训练器加载和初始化
    trainer = Trainer(config,model)

    # 模型训练
    best_valid_score, best_valid_result = trainer.fit(train_data, valid_data)

    ## 模型评估
    test_result = trainer.evaluate(test_data)
    print(test_result)

以下为完整的报错截图 以下是我的数据集样式 OS information:

OS: windows
RecBole Version 不太清楚
Python Version 3.6.12
PyTorch Version 1.7.0
cudatoolkit Version 11.1

新手上路，麻烦大大们了

Issue Analytics

State:
Created 3 years ago
Comments:11 (1 by maintainers)

Top GitHub Comments

6reactions

batmanflycommented, Nov 20, 2020

谢谢你提供的数据，前面的修复并不完全，应该将RecBole库中recbole/trainer/trainer.py从第313行至第322行的

extra_len_list = np.subtract(self.tot_item_num, tmp_len_list).tolist()
padding_nums = self.tot_item_num * len(tmp_len_list) - np.sum(tmp_len_list)
padding_tensor = torch.tensor([-np.inf], dtype=scores.dtype, device=self.device).repeat(padding_nums)
padding_scores = torch.split(padding_tensor, extra_len_list)

final_scores = list(itertools.chain.from_iterable(zip(pos_scores, neg_scores, padding_scores)))
final_scores = torch.cat(final_scores)

setattr(interaction, 'pos_len_list', pos_len_list)
setattr(interaction, 'user_len_list', len(tmp_len_list) * [self.tot_item_num])

修改为：

final_scores_width = max(self.tot_item_num, max(tmp_len_list))
extra_len_list = np.subtract(final_scores_width, tmp_len_list).tolist()
padding_nums = final_scores_width * len(tmp_len_list) - np.sum(tmp_len_list)
padding_tensor = torch.tensor([-np.inf], dtype=scores.dtype, device=self.device).repeat(padding_nums)
padding_scores = torch.split(padding_tensor, extra_len_list)

final_scores = list(itertools.chain.from_iterable(zip(pos_scores, neg_scores, padding_scores)))
final_scores = torch.cat(final_scores)

setattr(interaction, 'pos_len_list', pos_len_list)
setattr(interaction, 'user_len_list', len(tmp_len_list) * [final_scores_width])

修改代码后代码可正常运行并返回预期效果，感谢耐心解答，对初学者来讲很棒的项目。再次感谢

谢谢你指出来我们代码中的问题，我们将持续完善我们的代码，并且保持快速反馈、快速修正的开发态度，也欢迎你将此工具推荐给其他人试用。刚刚发布，确实难免存在问题。我们承诺将持续改善。谢谢！

–赵鑫

3reactions

chenyushuocommented, Nov 19, 2020

谢谢你提供的数据，前面的修复并不完全，应该将RecBole库中recbole/trainer/trainer.py从第313行至第322行的

extra_len_list = np.subtract(self.tot_item_num, tmp_len_list).tolist()
padding_nums = self.tot_item_num * len(tmp_len_list) - np.sum(tmp_len_list)
padding_tensor = torch.tensor([-np.inf], dtype=scores.dtype, device=self.device).repeat(padding_nums)
padding_scores = torch.split(padding_tensor, extra_len_list)

final_scores = list(itertools.chain.from_iterable(zip(pos_scores, neg_scores, padding_scores)))
final_scores = torch.cat(final_scores)

setattr(interaction, 'pos_len_list', pos_len_list)
setattr(interaction, 'user_len_list', len(tmp_len_list) * [self.tot_item_num])

修改为：

final_scores_width = max(self.tot_item_num, max(tmp_len_list))
extra_len_list = np.subtract(final_scores_width, tmp_len_list).tolist()
padding_nums = final_scores_width * len(tmp_len_list) - np.sum(tmp_len_list)
padding_tensor = torch.tensor([-np.inf], dtype=scores.dtype, device=self.device).repeat(padding_nums)
padding_scores = torch.split(padding_tensor, extra_len_list)

final_scores = list(itertools.chain.from_iterable(zip(pos_scores, neg_scores, padding_scores)))
final_scores = torch.cat(final_scores)

setattr(interaction, 'pos_len_list', pos_len_list)
setattr(interaction, 'user_len_list', len(tmp_len_list) * [final_scores_width])